Blog.

For decades, demand forecasting followed a relatively clear path. Forecasters relied on what customers expressed through purchases, search queries, and seasonal trends. These signals were observable, repeatable, and mostly predictable. Forecasting models based on historical data could produce reliable estimates. Supply chains could plan with a reasonable degree of confidence. That world is fading. Today, social media is reshaping how people discover products, interact with co

More and more businesses are using integrated AI and machine learning (ML) models within their ERP systems to forecast product demand but many struggle to explain these forecasts to management and colleagues. Why? Because AI and ML models are complex and operate in high-dimensional spaces, which are harder to understand than traditional models based on simpler, linear relationships. Even though these models have demonstrated that they can improve forecast accuracy, they are d

This article is part of a 3-part series exploring the key barriers preventing successful AI adoption in manufacturing and supply chains, based on recent research and industry insights. In  Part 1 , I examined organizational and cultural barriers.  Part 2  addressed the technical and data challenges. This final article focuses on the ethical and governance issues that shape responsible and sustainable AI adoption. In   Part 1   of this series, I examined how organizational and